Statistical Techniques for
Achieving Quality
Chapter 20
Achieving Quality Through Continual
Improvement
Claude W. Burrill / Johannes Ledolter
Published by John Wiley & Sons, Inc., 1999
Prepared by Dr. Tomi Wahlström,
University of Southern Colorado
Chapter 20
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Introduction
 Statistical quality control is a branch of
applied statistics dealing with statistical
tools that help control the quality of
products and improve the processes
responsible for these products
 Acceptance sampling plans rely on
samples of incoming or outgoing material
to decide whether a lot should be accepted
Chapter 20
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Introduction
 Control charts are important tools for
monitoring processes through the
products they produce
 Statistical design of experiments and
experimentation may also be used as a part
of statistical quality control
Chapter 20
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Sample Inspection
 When it is not realistic to use census for
inspection, one must rely on sample
inspection
• Taking a small sample size n from each
shipment lot, inspecting the sampled items,
and counting the number of items meeting the
requirements
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Control Charts
 Graphical tool used to understand the
production process and to help assure that
the quality of products produced by that
process is consistent (or stable) over time
 Easy to explain and apply
• Raise important issues and force their
resolution
• Focus attention on process rather than product
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X-bar Charts
 Also known as the mean chart
 Used in situations where one measures a
continuous variable
 Terms associated with it:
• Upper and lower control limits
• Stable process, constant cause system
• Assignable cause
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R-Charts
 Also known as the range chart
 Plots the ranges of samples taken over
time and monitors the variability of the
process over time
 Both X-bar and R-charts are used whenever
we measure so called “variable” aspects of
a process
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Control Charts for Attribute Data
 p-Charts
• Used to monitor the proportion of defective items
 c-Charts
• Used to keep track of the number of defects or
flaws on a product
 Both used for attribute data
• Data that describes the presence or absence of a
certain characteristic
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Process Capability
 Typically, a product must meet certain
specifications called a target value (Tg),
lower specification limit (LSL), and an upper
specification limit (USL)
 If the products meet the specifications, the
process is capable of producing according
to the given specifications
Capability Index (Cp)
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Statistical Design of Experiments
 Information can be collected in an active or
passive manner and must be collected
according to an experimental plan
 Important principles to consider:
• Randomization
• Blocking
– “Block whatever you can block, and randomize
whatever you can’t block.”
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Questions?
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